Abstract
Brain computer interface (BCI) allows persons to express their wishes, emotions. It is essential to connect the brain signals and computers. Through the BCI system, the multichannel Electroencephalogram (EEG) classification of Motor Imagery (MI) based on Common Spatial Pattern (CSP) can get a good result. However, when it comes to less channels, the classification’s result is not so practical. This paper focuses on the improvement of less channels’ accuracy. Through the methods of spectral feature and transformation based on Multivariate Empirical Mode Decomposition (MEMD), the accuracy can improve 15.5% on average. As a result, the methods are effective on this issue.
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